class_docstr: This `Field` represents a list of string tokens.  Before constructing this object, you need
  to tokenize raw strings using a :class:`~allennlp.data.tokenizers.tokenizer.Tokenizer`.
  
  Because string tokens can be represented as indexed arrays in a number of ways, we also take a
  dictionary of :class:`~allennlp.data.token_indexers.token_indexer.TokenIndexer`
  objects that will be used to convert the tokens into indices.
  Each `TokenIndexer` could represent each token as a single ID, or a list of character IDs, or
  something else.
  
  This field will get converted into a dictionary of arrays, one for each `TokenIndexer`.  A
  `SingleIdTokenIndexer` produces an array of shape (num_tokens,), while a
  `TokenCharactersIndexer` produces an array of shape (num_tokens, num_characters).
docstr: The `TextField` has a list of `Tokens`, and each `Token` gets converted into arrays by
  (potentially) several `TokenIndexers`.  This method gets the max length (over tokens)
  associated with each of these arrays.
docstr_tok: ["The","`","TextField","`","has","a","list","of","`","Tokens","`",",","and","each","`","Token","`","gets","converted","into","arrays","by","(","potentially",")","several","`","TokenIndexers","`",".","This","method","gets","the","max","length","(","over","tokens",")","associated","with","each","of","these","arrays","."]
code: def get_padding_lengths(self) -> Dict[str, int]:
      """
      The `TextField` has a list of `Tokens`, and each `Token` gets converted into arrays by
      (potentially) several `TokenIndexers`.  This method gets the max length (over tokens)
      associated with each of these arrays.
      """
      if self._indexed_tokens is None:
          raise ConfigurationError(
              "You must call .index(vocabulary) on a field before determining padding lengths."
          )
  
      padding_lengths = {}
      for indexer_name, indexer in self.token_indexers.items():
          indexer_lengths = indexer.get_padding_lengths(
              self._indexed_tokens[indexer_name]
          )
          for key, length in indexer_lengths.items():
              padding_lengths[f"{indexer_name}___{key}"] = length
      return padding_lengths
  
imports: from collections import defaultdict
  from copy import deepcopy
  from typing import Dict, List, Optional, Iterator
  import textwrap
  from spacy.tokens import Token
  import torch
  from allennlp.common.checks import ConfigurationError
  from allennlp.data.fields.sequence_field import SequenceField
  from allennlp.data.tokenizers import Token
  from allennlp.data.token_indexers.token_indexer import TokenIndexer, IndexedTokenList
  from allennlp.data.vocabulary import Vocabulary
  from allennlp.nn import util
label_desc: Use image node_id 6 for calling the TextField obj's underlying member method code with example usage: obj.get_padding_lengths() and returns: padding_lengths